Visualization of large molecular trajectories

The analysis of protein-ligand interactions is a time-intensive task. Researchers have to analyze multiple physico-chemical properties of the protein at once and combine them to derive conclusions about the protein-ligand interplay. Typically, several charts are inspected, and 3D animations can be played side-by-side to obtain a deeper understanding of the data. With the advances in simulation techniques, larger and larger datasets are available, with up to hundreds of thousands of steps. Unfortunately, such large trajectories are very difficult to investigate with traditional approaches. Therefore, the need for special tools that facilitate inspection of these large trajectories becomes substantial. In this paper, we present a novel system for visual exploration of very large trajectories in an interactive and user-friendly way. Several visualization motifs are automatically derived from the data to give the user the information about interactions between protein and ligand. Our system offers specialized widgets to ease and accelerate data inspection and navigation to interesting parts of the simulation. The system is suitable also for simulations where multiple ligands are involved. We have tested the usefulness of our tool on a set of datasets obtained from protein engineers, and we describe the expert feedback.Peer ReviewedPostprint (author's final draft

Decision-making process framework at the planning phase of housing development project

Every housing development project needs to go through several procedures which consist of a decision-making process. By practising the decision-making process since the planning phase, the relevant decision-maker is assisted in analysing and organising all issues arise such as the problem in identification and selection of a suitable contractor for housing development. However, the decisions are made without knowing precisely what will happen in the future. The research’s primary purpose is to develop a process model for decision-making at Malaysia’s housing development planning phase. This study also examines the decision-making process practised among Malaysian private housing developers at the planning phase and classifies four main aspects of decision-making: methods, tools, criteria and information. The study then discovers whether the four main aspects (methods, tools, criteria and information) are strongly related to the decision making process. This study comprises the development of a theoretical framework by integrating the models that have been developed by numerous authors and researchers on the subject of decision making. Besides, 67 private housing developers have been chosen as respondents for a questionnaire survey in this study. The descriptive statistical analysis and the correlated analysis are conducted employing the Statistical Package for Social Sciences (SPSS). The results of this study show different findings for every four main aspects studied. However, it still answers the research objectives, and the relationship between the four main aspects of the decision-making process is accepted. This study is useful because it serves as a guide for private housing developers and governments in decision making at the planning phase of housing development. Moreover, this study provides a new process framework for decision making at the planning phase of housing development in Malaysia and assists housing developers and governments to make better predictions before proceeding to the construction phase

Turbulent Mixing in Transverse Jets

Turbulent mixing is studied in liquid-phase transverse jets. Jet-fluid concentration fields were measured using laser-induced fluorescence and digital-imaging techniques, for jets in the Reynolds number range 1000 <= Re <= 20,000, at a jet-to-freestream velocity ratio of 10. Analysis of the measured scalar fields indicates that turbulent mixing is Reynolds-number dependent, as manifest in the evolving probability density functions of jet-fluid concentration. Enhanced homogenization is found with increasing Reynolds number. Turbulent mixing is also seen to be flow dependent, based on differences between jets discharging into a crossflow and jets into a quiescent reservoir. A novel technique for whole-field measurement of scalar increments was used to study the distribution of difference (scalar increments) of the scalar field. These scalar increments are found to tend toward exponential-tailed distributions with decreasing separation distance. Finally, the scalar field is found to be anisotropic, particularly at small length scales. This is seen in power spectra, directional scalar microscales, and directional PDFs of scalar increments. The local anisotropy of the scalar field is explained in terms of the global dynamics and large-scale strain field of the transverse jet

Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity

The clustering of data into physically meaningful subsets often requires assumptions regarding the number, size, or shape of the subgroups. Here, we present a new method, simultaneous coherent structure coloring (sCSC), which accomplishes the task of unsupervised clustering without a priori guidance regarding the underlying structure of the data. sCSC performs a sequence of binary splittings on the dataset such that the most dissimilar data points are required to be in separate clusters. To achieve this, we obtain a set of orthogonal coordinates along which dissimilarity in the dataset is maximized from a generalized eigenvalue problem based on the pairwise dissimilarity between the data points to be clustered. This sequence of bifurcations produces a binary tree representation of the system, from which the number of clusters in the data and their interrelationships naturally emerge. To illustrate the effectiveness of the method in the absence of a priori assumptions, we apply it to three exemplary problems in fluid dynamics. Then, we illustrate its capacity for interpretability using a high-dimensional protein folding simulation dataset. While we restrict our examples to dynamical physical systems in this work, we anticipate straightforward translation to other fields where existing analysis tools require ad hoc assumptions on the data structure, lack the interpretability of the present method, or in which the underlying processes are less accessible, such as genomics and neuroscience

Nonlinearity of Mechanochemical Motions in Motor Proteins

The assumption of linear response of protein molecules to thermal noise or structural perturbations, such as ligand binding or detachment, is broadly used in the studies of protein dynamics. Conformational motions in proteins are traditionally analyzed in terms of normal modes and experimental data on thermal fluctuations in such macromolecules is also usually interpreted in terms of the excitation of normal modes. We have chosen two important protein motors - myosin V and kinesin KIF1A - and performed numerical investigations of their conformational relaxation properties within the coarse-grained elastic network approximation. We have found that the linearity assumption is deficient for ligand-induced conformational motions and can even be violated for characteristic thermal fluctuations. The deficiency is particularly pronounced in KIF1A where the normal mode description fails completely in describing functional mechanochemical motions. These results indicate that important assumptions of the theory of protein dynamics may need to be reconsidered. Neither a single normal mode, nor a superposition of such modes yield an approximation of strongly nonlinear dynamics.Comment: 10 pages, 6 figure